This paper investigates the receiver design for clipped orthogonal time frequency space (OTFS) systems, where the user devices are equipped with power amplifiers (PAs) with low dynamic range. To improve power efficiency, the PAs have to work near the saturation points, which leads to unknown nonlinear distortions, thus making the signal detection more challenging. To solve this problem, techniques like intentional clipping or pre-distortion are adopted, thus approximating the outputs of the PAs as clipped signals. To further compensate for the unknown time-varying multipath channel and the clipping distortion at the receiver, the channel and clipping amplitude (CA) estimation, channel tracking, and signal detection are studied in this paper. Firstly, a receiver framework is developed for clipped OTFS. Secondly, by adopting the sparsity of the delay-Doppler (DD) domain channel and the piecewise linearized signal model with respect to CA, a novel sparse Bayesian learning (SBL) based joint channel and CA estimation scheme is proposed. Then, to further reduce the estimation error and bit error rate, a Kalman filter (KF) based channel tracking scheme and a minimum mean square error decision feedback blockwise equalization (MMSE-DFBE) based detection scheme are proposed. These two schemes are integrated in an expectation maximization (EM) based iterative tracking and detection algorithm. Finally, numerical simulations are conducted to demonstrate the superiority of the proposed schemes in terms of both estimation error and bit error rate.

With the rapid expansion of Internet of Things (IoT) applications, the demand of wide coverage and massive connectivity is inevitable. In this context, this paper investigates massive unsourced random access (URA) paradigm for low earth orbit (LEO) satellite IoT applications, focusing on device separation and signal detection. By exploiting the structured Grassmannian constellation to generate the codebook, a tensor-based URA transmission scheme is provided, which models the separation and detection problem as a general canonical polyadic (CP) decomposition. Then, to evaluate the access capability of our considered URA scheme, a comprehensive uniqueness analysis considering both sufficient conditions and necessary conditions is presented. Accordingly, an efficient generalized line-search-accelerated alternating least squares (GLSA-ALS) method is proposed to conduct the device separation and signal detection, which can avoid a large number of inverse computations for large-scale matrices. To be specific, with the help of the relaxation factors during the iteration, our proposed method can converge at a fast speed with negligible performance loss, which facilitates a better trade-off between the detection accuracy and computational complexity. Furthermore, depending on the demand of a specific application scenario, the flexible selection of relaxation factors enables the proposed method to be compatible to the classical ALS method, which can enhance the performance at the cost of additional complexity. Finally, relying on the maximum likelihood (ML)-based detection approach, the message list transmitted by active devices from one common codebook can be recovered. Simulation results demonstrate that the proposed GLSA-ALS method outperforms the state-of-the-art methods for practical LEO satellite IoT applications.

Beamforming is a key technology for achieving integrated sensing and communication (ISAC). However, most existing works focus on mono-static sensing, which has limited sensing accuracy and strong self-interference. To address these issues, this paper investigates hybrid beamforming (HBF) design for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) ISAC system with multi-static cooperative localization. Specifically, one access point (AP) simultaneously forms communication beams to serve multiple user equipments (UEs) and a sensing beam towards one target, and other multiple distributed APs perform cooperative localization on the target by estimating the angle-of-arrivals (AOAs) of received echo signals. First, to characterize the target localization accuracy, we derive the squared position error bound (SPEB) of AOA-based multi-static cooperative localization. Then, two HBF optimization problems are formulated to investigate the performance tradeoff between sensing and communication. For the sensing-centric design, we aim to minimize the SPEB of target localization while ensuring the signal-to-interference-plus-noise ratio (SINR) requirements of individual UEs. To tackle this nonconvex problem, we propose a semidefinite relaxation (SDR)-based alternating optimization algorithm. For the communication-centric design, a fractional programming (FP)-based alternating optimization algorithm is proposed for solving the communication sum-rate maximization problem under the sensing SPEB constraint. Simulation results demonstrate that the proposed two HBF algorithms can achieve localization accuracy and sum-rate performance close to fully-digital beamforming counterparts and outperform other baseline schemes.

Long-range (LoRa) can provide highly energy-efficient and cost-effective communications for low power wide area networks, playing an indispensable role in the Internet of Things (IoT). However, terrestrial LoRa networks cannot guarantee pervasive connectivity, especially in rural and remote areas. To tackle this problem, exploiting LoRa-based low Earth orbit (LEO) satellite IoT has garnered a growing interest in both academia and industry. In this paper, we provide a novel analytical framework based on spherical stochastic geometry (SG) for characterizing the uplink access probability of LoRa-based LEO satellite IoT. For practical modeling, multiple classes of LoRa end-devices (EDs) are taken into consideration, where each class of EDs is modeled by an independent Poisson point process (PPP). Both the channel characteristics of near-Earth satellite communications and the unique features of LoRa network are considered to derive closed-form analytical expressions for the uplink access probability. Numerical simulations validate the accuracy of our theoretical analysis and provide insightful guidelines for the practical design and implementation of LoRa-based LEO satellite IoT.

Beamforming is a key technique for achieving integrated sensing and communication (ISAC). However, most existing works focus on mono-static sensing, which can only provide limited sensing accuracy and range. In this paper, we investigate hybrid beamforming design for millimeter-wave (mmWave) multipleinput multiple-output (MIMO) ISAC system with multi-static cooperative localization, where one access point (AP) simultaneously transmits communication beams to serve multiple user equipments (UEs) and transmits a sensing beam towards a target, and other nearby APs perform cooperative localization on the target by estimating the angle-of-arrivals (AOAs) of received echo signals. To characterize the target localization accuracy, we derive the squared position error bound (SPEB) of AOA-based cooperative localization by using the equivalent Fisher information matrix (EFIM). Then, the hybrid beamforming design problem is formulated to minimize the SPEB of target localization, while satisfying the signal-to-interference-plus-noise ratio (SINR) requirements of individual communication UEs, transmit power budget, and constant modulus constraints. To solve the non-convex problem, a semidefinite relaxation (SDR)-based alternating optimization algorithm is proposed. Simulation results demonstrate that the proposed hybrid beamforming can achieve localization accuracy close to fully-digital beamforming and outperform the baseline schemes.

Integrated sensing and communication (ISAC) can realize communication and sensing functionalities simultaneously by sharing spectrum and hardware resources, where the sensing performance can be guaranteed by accurate range and velocity estimation. However joint range and velocity estimation inherently confronts the accuracy-complexity tradeoff. Therefore, a low-complexity joint range and velocity estimation algorithm is developed in this work, referred to as the particle swarm optimization reconstructed subspace multiple signal classification (PSO-RS-MUSIC). The proposed algorithm leverages optimized subspace reuse mechanisms to enhance estimation accuracy. To address the high complexity problem, the PSO-RS-MUSIC algorithm employs the particle swarm optimization (PSO) technique to replace the traditional spectral peak search, thereby reducing computational complexity significantly. Simulation results illustrate that the proposed algorithm outperforms the conventional RS-MUSIC algorithm, while the computational complexity is reduced by more than 90%.

In this paper, we investigate near-field hybrid beam-forming design for millimeter-wave (mmWave) integrated sensing and communication (ISAC) systems, where one base station (BS) equipped with large-scale antenna array simultaneously serves multiple communication users and performs target localization by exploiting the degrees of freedom in both angle and distance domains. First, to characterize the target localization accuracy, we analyze the squared position error bound (SPEB) for estimating the two-dimensional (2D) position of target. Then, the hybrid beamforming design is formulated to maximize the sum-rate of communication users, while guaranteeing the SPEB constraint of target localization, transmit power constraint, and constant modulus constraints. To tackle the nonconvex problem, we propose a fractional programming (FP) and successive convex approximation (SCA)-based block coordinate descent (BCD) algorithm. Simulation results demonstrate that the proposed hybrid beam-forming can achieve sum-rate close to fully-digital beamforming and outperform the baseline schemes.

With the rapid development of Internet of Things (IoT), efficient and reliable massive IoT device connections need to be widely supported in the upcoming next-generation communication networks, especially for emerging high-mobility scenarios. In this context, this paper investigates massive grant-free random access (GF-RA) in high mobility scenarios, focusing on active device detection (ADD) and channel estimation (CE) under fast time-varying channels. By exploiting the inherent low-rank structure of the observed pilot-signal-tensor, a tensor-based GF-RA transmission scheme is provided. On this basis, we propose a joint ADD and CE method based on the canonical polyadic (CP) model for both sourced and unsourced RA frameworks. More specifically, by remodelling the observation signal as a third-order tensor, the channel parameters can be grouped in the factor matrices of the CP model. However, the excessive number of potential device connections in massive GF-RA scenarios lead to excessively large dimensions of the factor matrices, thus resulting in severe ill-condition. To solve this problem, the Vandermonde structure of factor matrices is developed, which enables the effective exploitation of the tensor subspace for CP decomposition. Then, by utilizing the pre-allocated training precoders, an effective two-dimensional search method is proposed to jointly detect active devices and initialize the iterative estimation of channel parameters. Finally, due to the grouping situation, independent and coupled channel parameters are estimated by appropriate methods based on maximum likelihood (ML) and iterative updating, respectively. Moreover, the pre-allocation of training precoders can be unified to the unsourced RA scenarios, where the joint ADD and CE can be regard as a simple degenerate method compared to sourced RA. Simulation results demonstrate that the proposed tensor-based GF-RA framework outperforms the state-of-the-art schemes in terms of both ADD and CE performance.

Recently, Long-Range (LoRa) based low Earth orbit (LEO) satellite Internet of Things (IoT) has garnered growing interest from both academia and industry, since it can guarantee pervasive connectivity in an energy-efficient and cost-effective manner. In this paper, we provide a novel spherical stochastic geometry (SG) based analytical framework for characterizing the uplink access probability of LoRa-based LEO satellite IoT system. Specifically, multiple classes of LoRa end-devices (EDs) are taken into consideration, where each class of LoRa EDs is modeled by an independent Poisson point process (PPP). Both the channel characteristics of the satellite-to-Earth communications and the unique features of the LoRa network are considered to derive closed-form analytical expressions for the uplink access probability of such a new paradigm. Moreover, the non-trivial impact of the spreading factor, the ED’s density, the orbit altitude, and the satellite effective beamwidth on the system performance is thoroughly investigated. Extensive numerical simulations are conducted, which not only validate the accuracy of our theoretical analysis but also provide useful insights into the practical design and implementation of LoRa-based LEO satellite IoT system.

LoRa is one of the most prominent low-power wide area network (LPWAN) technologies for Internet of Things (IoT) applications. As the core technique of LoRa physical (PHY) layer, chirp spread spectrum (CSS) modulation is employed to support low power and long range communications. Although it provides a compelling tradeoff between coverage and data rate, the relatively low-spectral efficiency (SE) is still a limiting factor for its extensive applications. In this article, we propose two enhanced group-based CSS modulation schemes, named in-phase and quadrature group-based CSS (IQ-GCSS) and time domain multiplexed group-based CSS (TDM-GCSS), which can achieve much higher SE than the conventional LoRa modulation. The transmitter architectures of our proposed modulation schemes are presented along with both coherent and noncoherent detection methods. Moreover, an overall performance analysis of our proposed schemes is provided in terms of bit error rate (BER) and computational complexity. Numerical results not only validate the accuracy of our theoretical analysis but also demonstrate substantial performance improvements of our proposed schemes in terms of effective throughput compared to the classical counterparts.

In recent years, long-range (LoRa) has become one of the most prominent low-power wide-area network (LPWAN) technologies for the Internet of Things (IoT), which is based on a proprietary chirp spread spectrum (CSS) modulation (i.e., LoRa modulation). However, with the ever-increasing transmission demands of various IoT applications, the low-data-rate issue of LoRa modulation has become a critical bottleneck for its extensive deployment. To address this issue, we first formulate a unified framework for CSS-based waveform design and propose a novel layered group-based CSS (LGCSS) modulation scheme to achieve much higher spectral efficiency (SE) and data rate, thus accommodating a wider range of IoT applications. The complete transmitter architecture of LGCSS modulation is presented along with both coherent and non-coherent detection methods. Moreover, a comprehensive performance analysis of our proposed scheme is conducted in terms of orthogonality, bit error probability (BEP), and computational complexity. Extensive numerical simulations are conducted to verify the effectiveness of our theoretical analysis and the superiority of our proposed scheme compared to the traditional counterparts.

Abstract Within the evolving landscape of fifth‐generation (5G) wireless networks, the introduction of network‐slicing protocols has become pivotal, enabling the accommodation of diverse application needs while fortifying defences against potential security breaches. This study endeavours to construct a comprehensive network‐slicing model integrated with an attack detection system within the 5G framework. Leveraging software‐defined networking (SDN) along with deep learning techniques, this approach seeks to fortify security measures while optimizing network performance. This undertaking introduces network slicing predicated on SDN with the OpenFlow protocol and Ryu control technology, complemented by a neural network model for attack detection using deep learning methodologies. Additionally, the proposed convolutional neural networks‐long short‐term memory approach demonstrates superiority over conventional ML algorithms, signifying its potential for real‐time attack detection. Evaluation of the proposed system using a 5G dataset showcases an impressive accuracy of 99%, surpassing previous studies, and affirming the efficacy of the approach. Moreover, network slicing significantly enhances quality of service by segmenting services based on bandwidth. Future research will concentrate on real‐world implementation, encompassing diverse dataset evaluations, and assessing the model's adaptability across varied scenarios.

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.

Millimeter-wave (mmWave) extra-large scale multiple-input-multiple-output (XL-MIMO) is a promising technique for achieving high data rates in the upcoming sixth-generation communication networks. This paper considers an indoor massive Internet-of-Things (IoT) access scenario served by mmWave XL-MIMO, where the wireless channels exhibit spatial non-stationarity and the coexistence of far-field and near-field communication. By analyzing and exploiting such mmWave XL-MIMO channels, we propose a low-latency grant-free massive IoT access scheme based on joint active user detection (AUD) and channel estimation (CE). Specifically, by exploiting the common user activity in different pilot subcarriers and the block sparsity of the angular-domain XL-MIMO channels, we propose a low-complexity generalized multiple measurement vector-joint AUD and CE algorithm for efficient indoor massive access. Simulation results verify that the proposed solutions outperform the state-of-the-art greedy compressive sensing-based schemes in terms of AUD and CE performance.

Hybrid precoding has been considered as a promising technology for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, since it can achieve a tradeoff between system performance and hardware complexity. However, the optimal solution is difficult to obtain due to the coupling between analog precoder and digital precoder, as well as the non-convex constant modulus constraint, especially in multiuser scenarios. In this paper, we investigate several hybrid transceiver designs in wideband mmWave massive multiuser MIMO-OFDM systems for maximizing the spectral efficiency. Firstly, we propose two joint designs of hybrid precoder and combiner based on alternating optimization. Specifically, the intractable spectral efficiency maximization problem is reformulated as an equivalent weighted minimum mean square error (WMMSE) problem. To design the analog precoder and combiner with non-convex constant modulus constraint, we develop two efficient algorithms based on majorization minimization (MM) and element-wise block coordinate descent (EBCD) techniques, respectively. Secondly, to reduce the computational complexity, we propose a discrete Fourier transform (DFT) codebook based scheme, which can enhance the beamforming gain and mitigate the inter-beam interference. Thirdly, the convergence and complexity analysis are presented. The proposed two alternating optimization algorithms are guaranteed to converge to locally optimal solutions. Simulation results demonstrate that the proposed hybrid transceiver designs achieve significant performance gains over state-of-the-art schemes.

The integration of terrestrial and satellite communications (Satcom) is advocated for satisfying the challenging requirements of seamless, high-performance services. However, both the bandwidth and the power available are limited over satellite channels. In this paper, we propose index modulation (IM) and code-aided Satcom by conveying information by a pair of distinguishable constellation modes and their permutations. In order to combat both the linear and nonlinear distortion imposed by satellite channels, we conceive a factor graph (FG)-based iterative detection algorithm for Satcom relying on dual-mode (DM) IM (Sat-DMIM). The correlation amongst Sat-DMIM symbols imposed by both the channel-induced dispersion and the mode-selection mapping is explicitly represented by the FG constructed. Then the amalgamated belief propagation (BP) and mean field (MF) message passing algorithm is derived over this FG for detecting both the IM bits and the classic constellation mapping bits, while eliminating both the linear and nonlinear distortions. The complexity of the iterative detection algorithm is reduced by linearizing some high-order terms appearing in nonlinear distortion components using thea posterioriestimates of the Sat-DMIM symbols obtained from the previous iteration. Our simulation results demonstrate the power of the proposed amalgamated BP-MF-based and partial linearization approximation-based iterative detection algorithms.

The emerging space-air-ground integrated network has attracted intensive research and necessitates reliable and efficient aeronautical communications. This paper investigates terahertz Ultra-Massive (UM)-MIMO-based aeronautical communications and proposes an effective channel estimation and tracking scheme, which can solve the performance degradation problem caused by the uniquetriple delay-beam-Doppler squint effectsof aeronautical terahertz UM-MIMO channels. Specifically, based on the rough angle estimates acquired from navigation information, an initial aeronautical link is established, where the delay-beam squint at transceiver can be significantly mitigated by employing a Grouping True-Time Delay Unit (GTTDU) module (e.g., the designedRotman lens-based GTTDU module). According to the proposed prior-aided iterative angle estimation algorithm, azimuth/elevation angles can be estimated, and these angles are adopted to achieve precise beam-alignment and refine GTTDU module for further eliminating delay-beam squint. Doppler shifts can be subsequently estimated using the proposed prior-aided iterative Doppler shift estimation algorithm. On this basis, path delays and channel gains can be estimated accurately, where the Doppler squint can be effectively attenuated via compensation process. For data transmission, a data-aided decision-directed based channel tracking algorithm is developed to track the beam-aligned effective channels. When the data-aided channel tracking is invalid, angles will be re-estimated at the pilot-aided channel tracking stage with an equivalent sparse digital array, where angle ambiguity can be resolved based on the previously estimated angles. The simulation results and the derived Cramér-Rao lower bounds verify the effectiveness of our solution.

This paper proposes a low-complexity joint phase noise (PHN) estimation and decoding algorithm for orthogonal frequency division multiplexing relying on index modulation (OFDM-IM) systems. A factor graph (FG) is constructed based on the truncated discrete cosine transform (DCT) expansion model for the variation of PHN. In order to explicitly take into account the structured and sparse a priori information of the frequency-domain symbols provided by the soft-in soft-out (SISO) decoder, the generalized approximate message passing (GAMP) algorithm is employed. Furthermore, to solve the unknown and nonlinear transform matrix problem introduced by the PHN, the mean-field (MF) method is invoked at the observation nodes on the FG. Monte Carlo simulations show the superiority of the proposed algorithm over the existing variational inference (VI) and extended Kalman filter (EKF) methods in terms of their bit error rate (BER) performance and complexity. In addition, we demonstrate that the OFDM-IM scheme outperforms its conventional OFDM counterpart in the presence of PHN.

In this study, the authors investigate the secure transmission of a cooperative network, in which a source communicates with a destination via multiple cooperative nodes in the presence of a full‐duplex active eavesdropper, which can intercept the confidential signals and transmit jamming signals simultaneously. To safeguard the security of legitimate communication, two joint relay and jammer selection schemes are proposed according to the availability of the eavesdropper's channel state information, namely, optimal relay and random jammer selection scheme and optimal relay and optimal jammer selection scheme. The authors first derive the exact closed‐form expressions of the secrecy outage probability (SOP) for different selection schemes. Aiming at minimising SOP, they then adopt the deep feedforward neural network to determine the optimal power allocation between the selected relay and jammer. Further, the asymptotic expressions for SOP in the high signal‐to‐noise ratio regime are derived. Numerical results verify the analysis and demonstrate the performance advantage of the proposed scheme over conventional relay selection scheme with optimal power allocation.

In order to glean benefits from orthogonal frequency division multiplexing combined with index modulation (OFDM-IM) in the presence of strong Phase-Noise (PHN), in this paper, low-complexity joint PHN estimation and decoding methods are developed in the framework of message passing on a factor graph. Both the Wiener process and the truncated discrete cosine transform (DCT) expansion model are considered for approximating the PHN variation. Then based on these a factor graph is constructed for explicitly representing the joint estimation and detection problem. Taking full account of the sparse and structured a priori information arriving from the soft-in soft-out (SISO) decoder of a turbo receiver, a modified generalized approximate message passing (GAMP) algorithm is invoked for decoupling the frequency-domain symbols. In the decoupling step, mean field (MF) approximation is employed for solving the unknown nonlinear transform matrix problem imposed by PHN. Furthermore, merged belief propagation and MF (BP-MF) methods amalgamated both with sequential and parallel message passing schedules are introduced and compared to the proposed GAMP based algorithms in terms of their bit error ratio (BER) vs. complexity. Our simulation results demonstrate the efficiency of the proposed algorithms in the presence of both perfect and imperfect channel state information.

Hybrid analog-digital precoding is challenging for broadband millimeter-wave (mmWave) massive MIMO systems, since the analog precoder is frequency-flat but the mmWave channels are frequency-selective. In this paper, we propose a principal component analysis (PCA)-based broadband hybrid precoder/combiner design, where both the fully-connected array and partially-connected subarray (including the fixed and adaptive subarrays) are investigated. Specifically, we first design the hybrid precoder/combiner for fully-connected array and fixed subarray based on PCA, whereby a low-dimensional frequency-flat precoder/combiner is acquired based on the optimal high-dimensional frequency-selective precoder/combiner. Meanwhile, the near-optimality of our proposed PCA approach is theoretically proven. Moreover, for the adaptive subarray, a low-complexity shared agglomerative hierarchical clustering algorithm is proposed to group the antennas for the further improvement of spectral efficiency (SE) performance. Besides, we theoretically prove that the proposed antenna grouping algorithm is only determined by the slow time-varying channel parameters in the large antenna limit. Simulation results demonstrate the superiority of the proposed solution over state-of-the-art schemes in SE, energy efficiency (EE), bit-error-rate performance, and the robustness to time-varying channels. Our work reveals that the EE advantage of adaptive subarray over fully-connected array is obvious for both active and passive antennas, but the EE advantage of fixed subarray only holds for passive antennas.

In this work, we propose a secure communication scheme, where a source transmits information to the legitimate receiver in the presence of multiple eavesdroppers. To improve the security, single or multiple friendly jammers are deployed to confuse the eavesdroppers. Specifically, we assume that the jammers have to harvest energy from the source, thus we consider a two-phase transmission scheme where the source transmits energy to the jammers first and then transmits information to the legitimate receiver confidentially with the help of the jammers. We first give the expression of the secrecy outage probability, revealing how the secrecy performance depends on the transmission parameter tuple, and then we use the simulated annealing method to obtain the optimal transmission parameter tuple. The simulation results show the superiority of our proposed scheme.

In this paper, a linear precoding scheme is proposed to maximize the total throughput of a broadband geostationary earth orbit (GEO) satellite system based on traffic demands. The weighted max-min fair (WMMF) problem under the per-antenna power constraints and the traffic demands constraints is investigated to achieve this purpose. Semidefinite relaxation (SDR) approach and bisection search method are jointly adopted to solve the corresponding related problem, and then Gaussian randomization and a power control problem are proposed to get a suboptimal solution of the WMMF problem. Different traffic demands scenarios and weighted values selection schemes are considered to analyze the impact of them on the precoding design. Simulation results demonstrate that the total performance of the proposed scheme has been improved significantly over the traditional schemes and is better than the rate balance scheme.

Faster-than-Nyquist (FTN) signaling with index modulation (IM) is an attractive non-orthogonal transmission scheme characterized by high spectral efficiency and energy efficiency. In this paper, we develop a hybrid belief propagation (BP) and expectation propagation (EP) based iterative receiver for FTN-IM systems. To approach the optimal maximum a posteriori (MAP) receiver, we derive the factorization of marginal posterior probability and construct the corresponding factor graph by ignoring trivial interferences. To address the inherent colored noise imposed by FTN signaling, we employ autoregressive (AR) model to approximate the correlated noise samples. To further design low-complexity parametric message passing receiver, we resort to expectation propagation (EP) to derive Gaussian approximation of discrete transmitted symbols containing specific inactivated zeros. As a result, the overall complexity grows linearly with the number of transmitted symbols. Simulation results show that the coded FTN-IM system relying on the proposed iterative receiver can improve the spectral efficiency up to 43% without performance loss. For identical spectral efficiency with the Nyquist counterpart, FTN-IM signaling achieves 0.80 dB performance gain with proper packing factor and coding rate.

In this paper, we investigate the physical layer security of a dual-hop cooperative network in the presence of a full-duplex active eavesdropper, which can overhear the confidential signals and transmit jamming signals simultaneously. We utilize the optimal relay selection scheme to improve the secrecy performance, where the relay maximizing the secrecy capacity will be selected to forward the information. To evaluate the secrecy performance of our system, we derive a compact closed-form expression of the secrecy outage probability. Besides, we also analyze the asymptotic performance related to the position of the nodes. Finally, we verify our analysis through the numerical results, and demonstrate that there exists a secrecy protection region where the secrecy outage probability is below a target probability.

In this study, the authors propose a learning‐based approach to improve the security of the authors' considered communication system in a dynamic environment, where a source transmits information to a legitimate receiver in the presence of an active eavesdropper. Additionally, they assume that the source has to harvest energy from the environment to support its communication. Due to the dynamic of the environment, both the harvested energy and the channel vary over time, requiring a dynamic transmission strategy that follows the changes. In order to improve the security performance, they first analyse how to select the optimal transmission parameters in hindsight, and then they propose to combine the Q‐learning algorithm and the expert advice method to maximise the cumulative reward in the dynamic environment. They also introduce an improved learning‐based approach, which accelerates the convergence of their approach. The simulation results show that their proposed learning‐based approach helps the legitimate nodes learn a beneficial transmission strategy to obtain a larger cumulative reward.

In this study, a new scheme called adaptive hierarchical coding and modulation has been proposed, based on Adaptive Coding and Modulation (ACM) and hierarchical modulation (HM). The primary contribution of this study is summarised as the following aspects. Firstly, 16‐ to 64‐APSK constellations with size and labelling optimisation method are proposed for HM, which can be adapted to transmitting multiple hierarchies with different QoS streams and to the non‐linear satellite channels due to a lower peak‐to‐average power ratio compared to traditional hierarchical QAM modulation. Bit channel capacity and Chernoff error bound are introduced to optimise size and labelling of the constellation with interior‐point algorithm and modified binary switch algorithm for a specific channel model to get a better performance. Secondly, combining with optimised constellations, coding and modulation modes set is obtained from using constellation design and optimisation method, with different modulation orders and code rates. And over satellite channels with rain and location attenuation, the proposed scheme is able to adapt itself to a changing channel condition and increase the transmission rate compared with ACM schemes of DVB‐S2 and DVB‐S2X standard.

This paper proposes a closed-loop sparse channel estimation (CE) scheme for wideband millimeter-wave hybrid full-dimensional multiple-input multiple-output and time division duplexing based systems, which exploits the channel sparsity in both angle and delay domains. At the downlink CE stage, random transmit precoding matrix is designed at base station (BS) for channel sounding, and receive combining matrices at user devices (UDs) are designed whereby the hybrid array is visualized as a low-dimensional digital array for facilitating the multi-dimensional unitary ESPRIT (MDU-ESPRIT) algorithm to estimate respective angle-of-arrivals (AoAs). At the uplink CE stage, the estimated downlink AoAs, namely, uplink angle-of-departures (AoDs), are exploited to design multi-beam transmit precoding matrices at UDs to enable BS to estimate the uplink AoAs, i.e., the downlink AoDs, and delays of different UDs, whereby the MDU-ESPRIT algorithm is used based on the designed receive combining matrix at BS. Furthermore, a maximum likelihood approach is proposed to pair the channel parameters acquired at the two stages, and the path gains are then obtained using least squares estimator. According to spectrum estimation theory, our solution can acquire the super-resolution estimations of the AoAs/AoDs and delays of sparse multipath components with low training overhead. Simulation results verify the better CE performance and lower computational complexity of our solution over state-of-the-art approaches.

Tomlinson-Harashima precoding (THP) is a popular nonlinear interference mitigation technique employed at the transmitter side which can provide better performance in comparison with linear precoding technique with a slight increase in complexity. It can be considered as a dual to the successive interference cancellation (SIC). In this paper, we focus on a new THP scheme for the downlink of multiuser multiple-input multiple-output (MIMO) systems with decentralized receivers. In contrast to previous approaches, the proposed algorithm allows the provision of transmitting different types of information simultaneously subjected to different Quality of Service (QoS) requirements of each active user. This algorithm is based on minimum mean square error (MMSE) criterion and the closed-form solution of the processing matrices is found by applying Cholesky factorization with symmetric permutation. For this THP algorithm, by suitably ordering the rows of channel matrix we can obtain better performance. Moreover, it significantly improves the bit error rate (BER) performance as well as the achievable rate as compared with zero-forcing (ZF) THP available in the literature, which can be shown from simulation results.

Faster-than-Nyquist (FTN) signaling has attracted a lot of attentions for the fifth-generation (5G) cellular communication systems. However, low-complexity receiver design for FTN signaling becomes challenging. In this paper, we develop frequency-domain joint channel estimation and decoding methods for FTN signaling transmitting systems over frequency-selective fading channels. To deal with the colored noise inherent in FTN signaling, we propose to approximate the corresponding autocorrelation matrix by a circulant matrix, the special eigenvalue decomposition of which facilitates an efficient fast Fourier transform operation and decoupling the noise in frequency domain. Through a specific partition of the received symbols, many independent estimates are obtained and combined to further improve the accuracy of the channel estimation and data detection. Moreover, instead of assuming the data symbols to be Gaussian random variables, a generalized approximated message passing-based equalization is developed and embedded in the turbo iterations between the channel estimation and the soft-in soft-out decoder. Simulation results show that the proposed algorithm outperforms the cyclic prefix-based and overlap-based frequency-domain equalization methods. With the proposed algorithms, FTN signaling reaches up to 67% higher transmission rate compared to the Nyquist counterpart without substantially consuming more transmitter energy per bit, and the overall complexities grow logarithmically with the length of the observations.

In this paper, a code-aided maximum-likelihood and moment-based joint SNR estimator is proposed for M- ary amplitude phase shift keying (APSK) signals over AWGN channels. The proposed estimator significantly improves the performance at low SNRs by utilizing the syndrome in the LDPC codes to act as a reference measurement of estimation performance. Moreover, a methodology to measure the performance of the estimation by the syndrome of the LDPC codes is derived and the simulation results reveal that the number of 0s in the syndrome presents a positive correlation with the SNR. Compared with code-aided maximum-likelihood (ML) estimators and moment-based estimators for M-APSK signals, it is validated that the proposed joint SNR estimator has integrated the advantages of the classical approaches and simulation results also show that the proposed estimator exploiting the decision metric of the selector performs better at the SNR estimation range.

Vehicular Ad Hoc Networks (VANET) is an important network technology. Relay communication can effectively improve the connectivity and coverage of VANET, especially in distributed environments. Challenges arise from intense collision resulting from inherently synchronized relays. In this paper, we propose a decentralized relay scheme without collecting neighbor nodes' information. Particularly, we design a new score function to prioritize the relays based on their reception quality from source and channel conditions towards intended destination. A closed-form expression for packet delivery ratio (PDR) is derived based on time-out probabilities. Our analyses, validated by simulations, show that the proposed scheme, in terms of PDR, is much better than DAFMAC protocol.

Modern wireless communication raise the demand for higher spectral efficiency, faster-than-Nyquist (FTN) signaling is able to increase transmission rate without expanding signaling bandwidth. In this paper, we develop a graph-based iterative FTN detector in the presence of phase noise (PHN). Wiener process is employed to model the time evolution of nonstationary channel phase. The colored noise imposed by sampling of FTN signaling is approximated by autoregressive model. Based on the factor graph constructed, messages are derived on the two subgraphs, i.e., PHN estimation subgraph, and the FTN symbol detection subgraph. We propose a combined sum-product and variational message passing (SP-VMP) method to update the messages between subgraphs, which enables low- complexity parametric message passing and provides closed-form expressions for parameters updating. Simulation results show the superior performance of the proposed algorithm compared with the existing methods and verify the advantage of FTN signaling compared with the Nyquist counterpart.

Location-aware wireless networks can provide precise location information in harsh environments, however, which is only possible when all nodes are well-functioning. In this paper, we propose algorithms and analyze the performance limits for both non-cooperative and cooperative localization networks in the presence of ranging outliers. Especially, we show that the localization performance can be boosted by using the cooperative outlier detection scheme. An algorithm based on expectation-maximization is proposed for non-cooperative localization networks, while a variational message passing-based algorithm is proposed for the cooperative counterparts. Performance limits are investigated using Cramér-Rao lower bound. Further inspection on the performance limits confirms the performance gain from the cooperative detection scheme. Stochastic geometric analysis is also carried out to account for the stochastic nature of wireless networks, as well as to provide simpler expressions and additional insights. Simulation results corroborate the analytical results, and show that both of the proposed algorithms are capable of attaining the corresponding performance limits at a significantly reduced computational cost compared with existing algorithms.

In this paper, we develop a joint channel estimation and decoding method for faster-than-Nyquist signaling (FTNS) transmitting over (quasi-static) time-varying frequency-selective fading channels based on the variational Bayesian (VB) framework. In contrast to existing methods, ours is capable of performing explicit frequency-domain channel estimation and decoding in a turbo mode without requiring any cyclic prefix (CP), as well preserving the computational complexity at a logarithmic level. In view of the colored noise inherent in FTNS, we propose to approximate the corresponding autocorrelation matrix by a circulant matrix, the special eigenvalue decomposition of which facilitates an efficient fast Fourier transform operation and decoupling the noise in frequency domain. In addition, through a specific partition of the received symbols, many independent estimates are obtained and combined to further improve the accuracy of the channel estimation and data detection. Simulation results show that the proposed algorithm outperforms the conventional CP-based and overlap-based frequency-domain equalization methods with known channel impulse response (CIR). Moreover, ours come within 1dB of the counterpart Nyquist system with 25% higher spectral efficiency achieved when the CIR is unknown.

Modern mobile communication applications raise the requirement of high quality support for high mobility users. In this paper, we present a Bayesian graphical model based frequency domain equalization method for faster-than-Nyquist (FTN) signaling in doubly selective channels. The conventional frequency domain minimum mean squared error (FD-MMSE) equalizer suffers high complexity due to the interferences induced by adjacent frequency symbols. To tackle this problem, a low complexity iterative message passing method namely, belief propagation is employed on the Bayesian graphical model to detect the FTN symbols. Compared to the low complexity variational inference method, the proposed algorithm considers the conditional dependencies between symbols and therefore can improve the performance. Simulation results show that the proposed equalization method has similar performance of the MMSE equalizer and outperforms the variational inference method.

Obtaining the location of a “passive” target in wireless sensor networks has attracted numerous interest in recent years. This paper considers the passive localization based on time-of-arrival measurements in an asynchronous sensor network where the receivers are with both clock skew and offset. Based on the factor graph model, the beliefs (approximated marginal) of target location and clock parameters can be obtained by executing iterative message passing algorithms. To reduce the huge complexity of particle based method, we propose two approximate approaches to determine parametric Gaussian message passing. Simulation results show that the proposed low complexity algorithm performs close to the particle-based method and attain the Cramer-Rao bound.

A maximum likelihood-based code-aided joint carrier phase estimation and ambiguity resolution algorithm is proposed for coded amplitude and phase shift keying (APSK) signals. The proposed estimator iteratively uses the a posteriori probability of coded bits obtained from the channel decoder to improve the performance of phase estimation and ambiguity resolution. Two initialization schemes are employed for systems with and without pilot symbols, which reduce the number of initial phase values required to bootstrap the iterative estimation algorithm. Compared with the existing estimators, simulation results demonstrate the performance improvement of the proposed algorithm in both mean-squared estimation error and bit error rate with lower computational complexity.

Oscillator phase noise (PHN) can result in significant performance loss in coherent communication systems if not compensated appropriately. Most existing studies focus on either PHN estimation over additive white Gaussian noise channels or channel impulse response (CIR) estimation in the absence of PHN. In this study, joint CIR estimation and decoding over time‐selective flat‐fading channels impacted by PHN is studied. Both the time evolutions of CIR and PHN are approximated by autoregressive models. Building on this, factor graph of the joint a posteriori probability function is constructed and the sum–product algorithm is applied to derive messages on factor graph. Due to the non‐linearity of PHN, no closed‐form expressions of the messages can be obtained. To this end, the authors use canonical distribution approach, which approximates the messages by Gaussian and Tikhonov probability density functions on the sub‐graphs of CIR and PHN, respectively. Accordingly, the messages can be calculated by updating the parameters of the canonical distributions. A mixed serial‐parallel message passing schedule is presented to implement the algorithm, which enables the compromise between the bit error rate performance and the processing throughput. Simulation results show that the proposed joint estimation and decoding algorithm significantly outperforms the existing methods in fading channels impacted by PHN.

This work deals with frequency-domain equalization for faster-than-Nyquist (FTN) signaling in doubly selective channels (DSCs). To handle the interference of frequency-domain symbols, the minimum mean square error (MMSE) equalizer involves high complexity in DSCs. To overcome the problem, we propose low-complexity receivers based on two variational methods, i.e., mean field (MF) and Bethe approximations. Compared with the MF method, the Bethe approximation takes into account the conditional dependencies of pairwise symbols. By only considering a small set of the frequency-domain symbols that have strong interference to each other, the complexity of the proposed algorithms increases linearly with the block length. Simulation results demonstrate that the proposed algorithms for FTN signaling are able to perform close to the MMSE equalizer in DSCs while with significantly reduced computational complexity.

In this paper, we study the time of arrival (TOA)-based distributed passive localization in asynchronous wireless network. Performing synchronization between receivers before target localization is possible but costs extra energy and bandwidth. To this end, We propose an expectation maximization (EM) algorithm to locate the passive target in the presence of receivers' clock offsets. To improve the robustness of the proposed algorithm, we employ the average consensus scheme to obtain the location of target at each receiver in a distributed way. A quadratic polynomial approximation is proposed to reduce the communication overhead and computational complexity. To evaluate the performance of the proposed algorithm, the Cramer-Rao bound (CRB) of the target's position estimation is derived. Simulation results show that the proposed distributed EM algorithm performs close to the centralized counterpart. It outperforms the conventional two step estimation method and the one based on time difference of arrival. Moreover, the proposed algorithm can attain the derived CRB, which demonstrates the effectiveness of the algorithm.

In wireless sensor networks, acquiring accurate timing information is a crucial requirement for time-based sensor localization. Utilizing a joint localization and synchronization method in sensor networks can improve positioning speed and accuracy. In this paper, we present a unified factor graph framework based on time of arrival (TOA) measurements to solve the problem of joint localization and time synchronization. A novel distributed cooperative joint estimation method based on belief propagation (BP) is proposed. We linearize the nonlinear terms in messages on factor graph in order to obtain a closed Gaussian form solution of message update. Accordingly, only the means and variances have to be updated and transmitted, which significantly reduce the communication overhead and computational complexity. To further reduce the communication overhead, we propose a message passing schedule. Simulation results show that the proposed BP method reach close performance to particle-based approaches with lower complexity.

Baseband predistortion is a popular and efficient method to linearize high power amplifier (HPA) in wireless communication systems. Polynomial (POLY) and look-up-table (LUT) are two methods to design baseband predistorter (PD). However, on the one hand, POLY-based method is complex to implement. On the other hand, LUT-based predistorter suffers convergence time and quantization error problem. In this paper, we propose a hybrid POLY and LUT predistorter for memory nonlinear system in wideband scenarios, it is also suitable for memoryless channel. Simulations show that the proposed hybrid structure outperforms the traditional one with lower complexity.

The evaluation of Cramer-Rao Bounds (CRBs) for phase estimation of coded linearly modulated signals are difficult due to the intractable expectations of the likelihood function with respect to coded symbols. In this paper, we propose two methods towards this end. The first one is a semi-analytical method for coded QPSK signals. Based on Gaussian approximation of extrinsic information, the expression of CRB is derived in terms of signal-to-noise ratio (SNR) and the mean of extrinsic information in closed form. For high-order modulations, e.g., 16QAM signal, we propose a numerical method based on multidimensional Gauss-Hermite Quadrature (GHQ). It is shown that, without suffering from the linearization error, the results of numerical method by GHQ outperform the semi-analytical results, and the former are consistent with that of the Monte Carlo simulations for systems with different codes and numbers of decoding iterations.

Localization in wireless sensor networks has become an attractive research field in recent years. Most studies focus on the mitigation of measurement noise by assuming the positions of anchors are perfectly known, which may become impractical due to some inevitable errors in the observations of anchors' positions. This paper addresses the problem by taking into account the a priori position information of inaccurate anchors. Considering that the maximum likelihood (ML) algorithm suffers from the intractable integrals involved, we resort to expectation maximization (EM) algorithm to solve this problem iteratively. The a posteriori probability of the anchor position is approximated by circularly symmetric Gaussian distribution, with parameters optimized by minimizing Kullback-Leibler divergence of the two distributions. Building on this approximation, we are able to derive the expectation step in closed form. Particle swarm optimization is then followed to perform the maximization step. Numerical results demonstrate that the proposed EM estimator is less sensitive to the anchors' uncertainties and it significantly outperforms the traditional ML estimator which ignores the prior information of anchors.

Localisation in wireless sensor networks (WSNs) has received much attention, where most studies focus on mitigating the effects of measurement noise under the assumption of accurate anchors’ positions. However, anchors’ positions could be inaccurate for the inevitable errors in practical observations. This paper studies the sensor localisation with both inaccurate anchors’ positions and noisy range measurements in WSNs. To solve the intractable integrals in likelihood function, the authors propose to use expectation‐maximisation (EM) algorithm to obtain the maximum likelihood (ML) estimation iteratively. The ‘a posteriori’ distribution of the anchor's position uncertainty is approximated to a circularly symmetric Gaussian distribution by minimising the Kullback‐Leibler divergence between them. Building on this, the authors derive the expectation step in a closed‐form expression. In the maximisation step, based on the Taylor expansion of the confluent hypergeometric function of the first kind presented in the expectation step, analytical solutions are obtained. Simulation results show that the proposed EM estimator significantly outperforms the approximated ML estimator. The performance gain by using the EM estimator becomes larger as the increase of anchors’ position uncertainties. Moreover, the performance of the EM estimator is close to that of the Monte Carlo‐based estimator with much less computational complexities.

A code-aided (CA) iterative signal-to-noise ratio (SNR) estimator based on Expectation Maximization (EM) algorithm is proposed for M-ary amplitude phase shift keying (APSK) signals. The estimation algorithm utilizes a posteriori probabilities of coded bits obtained from channel decoder to improve estimation precision at low SNRs. Furthermore, Cramer-Rao bound (CRB) of the proposed CA iterative SNR estimator for M-APSK is derived and simulated numerically. Compared with the non-data-aided (NDA) EM-based estimator and moments-based estimators for M-APSK signals, computer simulation results show that the proposed estimator exploiting a posteriori information has more excellent performance, especially at low SNRs. It is also demon-strated that the performances of the proposed CA SNR estimator for 16- and 32-APSK signals are very close to the derived CRB.

This paper presents a message passing approach to joint channel estimation, data detection and decoding over time-selective Rayleigh fading channel with residual carrier frequency offset (CFO). The proposed algorithm utilizes the sum product algorithm (SPA) implemented on a factor graph (FG) representing the joint a posteriori probability distribution of the unknown CFO, information bits and channel coefficients vector given the channel output. A combination of particle filtering and Gaussian parameterization is employed to approximate the exact probability density function in message passing for CFO and channel estimation. Computer simulations demonstrate the effectiveness of the proposed algorithm in combating the CFO over unknown Rayleigh fading channels.

Code-aided (CA) iterative carrier phase synchronizer can improve the phase estimation performance significantly. However, due to the coupling involved between phase recovery and decoding, most studies depend on extensive simulations rather than on theoretical analysis to evaluate the performance of CA phase recovery. In this paper, we propose analytical methods to fill this void. The first step is to model the cross-talks caused by phase offset as an additional Gaussian noise at low signal-to-noise ratios (SNRs). Then, a semi-analytical method is proposed to express the distribution of extrinsic information from channel decoder as a function of phase offset. Building on this model, both the open-loop and closed-loop performance of CA iterative hard/soft decision-directed phase synchronizers are derived in closed-form. The analytical results explicitly reveal how extrinsic information contributes to the performance improvement of carrier phase estimation. Monte Carlo simulation results corroborate that the proposed methods are able to accurately characterize the performance of CA iterative carrier phase recovery for systems with different channel codes.

In this paper, we propose a novel signal-to-noise ratio (SNR) estimation method for linear modulations on additive white Gaussian noise (AWGN) channel. It estimates noise power directly without estimating the received total power. The estimation mean and mean square error (MSE) are analyzed theoretically and verified by simulations. Results show that the proposed SNR estimator performs better at low SNR than the conventional estimators while it suffers a little degradation at high SNR. Moreover, the proposed estimator can be implemented with less real multiplications than the conventional ones.

In this paper, we propose an improved iterative receiver scheme for low density parity check (LDPC) codes transmitted over unknown channels affected by a strong phase noise. For achieving joint channel parameter estimation, data detection and decoding, the proposed algorithm utilizes the sum product algorithm (SPA) implemented on the factor graph (FG) that represents the joint a posteriori probability of information symbols and channel parameters given the channel output. Through the iterative use of the soft information on coded symbols from channel decoder, the proposed algorithm employs forward-backward recursions for message passing on the graph. Numerical results for binary LDPC codes show that, the proposed algorithm can be able to cope with a strong phase noise under unknown channel information and achieve nearly the same performance as the optimal coherent receiver under DVB-S2 compliant ESA phase noise model, and only a slightly decrease under strong Wiener model.

Code-aided (CA) iterative carrier phase synchroniser can greatly improve the accuracy of phase estimation in turbo receivers. However, because of the iteration involved between phase recovery and decoding, most existing studies depend on extensive simulations rather than on theoretical analysis to evaluate the performance improvement of phase recovery by exploiting the coding constraints. In this study, the authors propose analytical methods to fill this void. The first step is to approximate the cross-talks caused by phase offset as Gaussian noise at low signal-to-noise ratios. Then, a semi-analytical method is proposed to express the distribution of extrinsic information from channel decoder as a function of phase offset. Building on this model, both the open-loop and closed-loop performances of CA iterative phase synchronisers are derived in closed-form. The analytical results explicitly reveal how extrinsic information contributes to the performance of carrier phase estimation. Monte Carlo simulation results corroborate that the proposed methods are able to accurately characterise the performance of CA iterative carrier phase recovery for systems with different channel codes.

This study studies the feedforward (FF) non-data-aided (NDA) carrier phase estimation of the amplitude and phase shift keying (APSK) signals. The true Cramer–Rao bounds (CRBs) for NDA phase estimation of APSK signals are derived and evaluated numerically using Gauss–Hermite quadrature. The jitter variance of the FF Viterbi–Viterbi (V&V) algorithm is analysed assuming the absence of data pattern noise. It is proved that, when the design parameter μ=2, the jitter variance is able to converge asymptotically to the modified CRB (MCRB) at high signal-to-noise ratios (SNRs). For practical application, the parameter μ is also optimised for 16/32/64-APSK signals at different SNRs. The analytical results of the jitter variance are verified by Monte-Carlo evaluations. It is shown that, for 32/64-APSK signals, the plain V&V algorithm cannot approach the CRBs due to the data pattern noise. A modified V&V algorithm based on the constellation partition and a linear combination of the sub-estimators is proposed to eliminate the divergence. Simulation results show that the jitter variance of the proposed estimator is very close to the CRB at the SNRs of interest and converges to the MCRB at high SNRs.

We analyze the performance of a code-aided (CA) decision-directed (DD) timing synchronizer, which can exploit the dependence structure across coded symbols to improve the timing recovery accuracy. Due to the inherent coupling between timing recovery and decoding, most existing studies rely on extensive simulation rather than on analytical methods to evaluate performance of timing recovery for coded systems. We propose analytical methods in this paper towards this end. A first key step is to approximate timing-offset-induced inter-symbol interference (ISI) as an additive Gaussian noise, since in the low signal-to-noise ratio (SNR) regime the background noise is large enough to mask the ISI. Then, we derive semi-analytical expressions for the mean and variance of extrinsic information as functions of timing offset, building on which we characterize both open-loop and closed-loop performance of decision-directed timing synchronizers. Monte Carlo simulation results corroborate that the proposed method accurately characterizes the performance of CA DD timing recovery, for systems with a wide range of channel bandwidth and different channel codes.

In this paper, we consider the effect of different rules of symbol decision on the performance of decision-directed synchronizers for LDPC-coded systems. Different from the conventional hard symbol decision based on the Maximum-A- Posteriori (MAP) criterion, soft symbol decision can be considered as the Minimum-Mean-Square-Error (MMSE) estimation of the transmitted symbol. By whether or not the coding constraints are taken into account, soft symbol decision is derived in non-code-aided (NCA) mode and code-aided (CA) mode, respectively. The performance of the soft decision-directed (SDD) synchronizer is compared with that of the hard decision-directed (HDD) counterpart in both NCA and CA mode. Simulation results show that, without much implementation complexity increase, the jitter performance of the synchronizer in CA mode significantly outperforms that in NCA mode. It is also observed that, in the scenario of this paper, SDD synchronizer works only slightly better than HDD synchronizer.

In this paper, based on analyzing some existing phase lock detectors for quadratic phase-shift keying (QPSK),we propose a novel method of estimating the signal-to-noise ratio (SNR) operating with the lock metric value of classical Mth-power (M=4) phase lock detector. The proposed lock detector-based SNR estimator can perform better than the conventional SNR estimator in terms of mean estimated value (MEV) at medium to high SNR when phase recovery loop is in-lock status. And its normalized mean squared error (NMSE) can reach Cramer-Rao bounds (CRBs) at that SNR. Moreover, the computational complexity of new estimator operating in phase recovery loop is analyzed with less additional multiplications than that of conventional one. This estimation method can also be applied to other similar phase lock detectors.

An improved data-aided (DA) frequency error detector (FED) and a frequency lock detector are proposed under large frequency offset for Digital Video Broadcasting Satellite Second Generation (DVB-S2) system. Computer simulations results show that the proposed error detector can increase the frequency acquisition range and decrease the acquisition time without complexity increase. Its closed-loop normalized frequency root mean square error (RMSE) improves at least 1.5 dB compared with that of conventional error detector. The proposed lock detector shows good lock indication. Its modified version can save more symbols to indicate the locking status.

For the symbol timing recovery techniques which are susceptible to carrier phase offset, clockless phase recovery is necessary in advance. In this paper, we propose a clockless nondata-aided (NDA) feedback phase error detector (PED) for M-ary phase-shift keying (MPSK) signal. Its derivation is given based on maximum likelihood (ML) criterion. The clockless NDA (Mthpower)PED can also be generalized with a design parameter 0≤m≤M to improve performance at low SNR. The openloop S-curves and closed-loop phase error variances of these PEDs are given with extensive simulations. Results show that the S-curve of clockless Mth-power PED is robust to signal-tonoise ratio (SNR). Its phase error variance is investigated with excellent performance under different shaping roll-off factors and parameter m. We also analyze the clockless decision-directed (DD) PED which may not be a good choice for over-sampled signals due to its unstable equilibrium point of S-curve.

Data-aided (DA) signal-to-noise ratio (SNR) estimation is required especially at low SNR. The conventional maximum likelihood (ML) DA SNR estimator requires perfect carrier phase estimation and frequency recovery. In this paper, we propose a novel carrier frequency robust DA SNR estimator with its improved variant using autocorrelation of received MPSK symbols. Computer simulations are used to examine their performance in terms of mean estimation value (MEV) and normalized mean square error (NMSE). For the example system in simulations, the MEV of proposed estimator is accurate enough with normalized frequency error on the order of symbol rate. However, its NMSE can not reach DA normalized Cramer-Rao bound (NCRB) even with large observatory length, whereas its NMSE may perform a little worse at high SNR for short pilot symbols. On the other hand, fortunately the its improved variant can reach NCRB with enough pilot symbols. What's more, the proposed DA SNR estimators can operate under large frequency errors or before the frequency recovery unit with baud rate. The implementation complexity is also analyzed.

In this study, the authors propose an exact maximum likelihood (ML) signal-to-noise ratio (SNR) estimator for coded linearly modulated signals. The estimator is expressed in terms of the marginal a posteriori probabilities (APPs) of the coded symbols, which can be obtained efficiently by the Bahl–Cocke–Jelinek–Raviv (BCJR) algorithm for codes defined on trellises. Simulation results show that the proposed ML code-aided (CA) SNR estimator significantly outperforms the non-data-aided (NDA) estimators in the low SNR regime. The Cramer–Rao bound (CRB) for CA SNR estimator is also derived and evaluated numerically. It is shown that the proposed ML-CA estimator performs very close to the derived bound. Comparisons of the CRBs for CA and NDA scenarios with different linearly modulated signals further illustrate the intrinsic performance improvement by exploiting the channel coding constraints.

In the above-referenced paper, equations (18) and (19) in page 691 are incorrectly printed. They are corrected herein.

A modified M&M frequency estimation algorithm for DVB-S2 system is proposed in this paper. This estimator provides a larger estimation range compared to the well known Fitz and L&R methods and achieves CRB in the whole SNR operation range of DVB-S2, with a little increase in computational complexity. The enlarged estimation range will reduce the acquisition time of the coarse frequency synchronizer in a two-step carrier frequency recovery scheme. Minimum accumulation lengths to achieve a certain RMS frequency estimation error for the L&R and modified M&M estimator at different SNR are presented. A variable accumulation length scheme based on SNR estimation or coding/modulation scheme employed in system is proposed to minimize the acquisition time in the fine frequency recovery.

In this paper we present M-TORA, a new multi-path routing protocol based on temporally-ordered routing algorithm (TORA) for mobile ad hoc networks (MANETs). Although TORA is a highly adaptive distributed routing algorithm, it can not make the most use of multiple paths which are already built and maintained in the process of route creation. We designed M-TORA for unleashing the potential power of multiple paths and optimizing performance of classical TORA. According to the modification of Internet MANET encapsulation protocol (IMEP), a node can spread its MAC layer information to its neighbors. The MAC information and the route hop count will be used in the calculation of route selection probability when a node has multiple paths to the destination. By using the probability routing algorithm, M-TORA can spread the network overload to multiple routing paths, which will lead to automatic load balancing. In simulation based on OPNET we show that M-TORA can outperform classical TORA in decreasing packet end-to-end delay, improving network packet delivery ratio and achieving fair node's local energy consumption.

This paper introduced a system employing hybrid DS/FH spread spectrum (SS) coupled with RS forward error-control coding. By the flexible redefinition of frequency-occupation and frequency-collision event, the frequency-occupation probability of the DS/FH SS network was analyzed. This probability was based on the simultaneous transmission number threshold and discussed in both synchronous and asynchronous circumstances, respectively. The network throughput based on the packet correct reception probability was analyzed. Two models which had finite and infinite population respectively was discussed. Finally, simulation results were given.

An analysis of "unequal power control (UPC)" for the general integrated services is presented based on our former discussion about UPC for voice/data integrated services. This optimal control problem is clarified first, and then an approximate solution is derived by linear programming. For practical application, we propose an implementation scheme based on simulation. Numerical results show that unequal power control can expand the "allowable load area" and provide a significant improvement in system capacity comparing with equal power control.